Summary of Position: Do Not Explain Vision Models Without Context, by Paulina Tomaszewska et al.
Position: Do Not Explain Vision Models Without Context
by Paulina Tomaszewska, Przemysław Biecek
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the limitations of current explanation methods for computer vision models, which fail to consider contextual information. Popular methods are reviewed and shown to be ineffective in certain scenarios, while real-world use cases demonstrate the importance of context. The authors propose new research directions that incorporate contextual information and argue that a shift is needed from focusing on “where” to understanding “how”. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how computer vision models can better explain their decisions by considering the context surrounding the objects they detect. Current methods don’t take into account this crucial information, leading to failures in specific situations. The authors highlight real-world applications where context matters and suggest new approaches that incorporate spatial context to improve explanations. |
